






'''simple docstring'''







class   __SCREAMING_SNAKE_CASE       :


  """simple docstring"""


  def __init__(			self	):






         """simple docstring"""


         a_						     =			{}  # Mapping from char to TrieNode
         a_						     =			False







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         for word in words:
                self.insert(UpperCamelCase__	)







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			self
         for char in word:
                if char not in curr.nodes:
                       a_						     =			TrieNode()
                a_						     =			curr.nodes[char]
         a_						     =			True







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			self
         for char in word:
                if char not in curr.nodes:
                       return False
                a_						     =			curr.nodes[char]
         return curr.is_leaf







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         def _delete(UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	) -> bool:
                if index == len(UpperCamelCase__	):
                       # If word does not exist
                       if not curr.is_leaf:
                              return False
                       a_						     =			False
                       return len(curr.nodes	) == 0
                a_						     =			word[index]
                a_						     =			curr.nodes.get(UpperCamelCase__	)
                # If char not in current trie node
                if not char_node:
                       return False
                # Flag to check if node can be deleted
                a_						     =			_delete(UpperCamelCase__   ,	UpperCamelCase__   ,	index + 1	)
                if delete_curr:
                       del curr.nodes[char]
                       return len(curr.nodes	) == 0
                return delete_curr

         _delete(self   ,	UpperCamelCase__   ,	0	)





def       __UpperCamelCase					(      lowercase_       :  TrieNode   ,       lowercase_       :  str   ):
       """simple docstring"""




       if node.is_leaf:
              print(lowercase_   ,       end=' '   )

       for key, value in node.nodes.items():
              print_words(lowercase_   ,       word + key   )


def       __UpperCamelCase					(      ):
       """simple docstring"""




       a_						     =			'banana bananas bandana band apple all beast'.split()
       a_						     =			TrieNode()
       root.insert_many(lowercase_   )
       # print_words(root, "")
       assert all(root.find(lowercase_   ) for word in words   )
       assert root.find('banana'   )
       assert not root.find('bandanas'   )
       assert not root.find('apps'   )
       assert root.find('apple'   )
       assert root.find('all'   )
       root.delete('all'   )
       assert not root.find('all'   )
       root.delete('banana'   )
       assert not root.find('banana'   )
       assert root.find('bananas'   )
       return True


def       __UpperCamelCase					(      lowercase_       :  str   ,       lowercase_       :  bool   ):
       """simple docstring"""




       print(str(lowercase_   )   ,       'works!' if passes else 'doesn\'t work :('   )


def       __UpperCamelCase					(      ):
       """simple docstring"""




       assert test_trie()


def       __UpperCamelCase					(      ):
       """simple docstring"""




       print_results('Testing trie functionality'   ,       test_trie()   )


if __name__ == "__main__":
  main()












'''simple docstring'''







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  _a  : List[Any]      			=     ['''image_processor''', '''tokenizer''']
  _a  : List[Any]      			=     '''ViTImageProcessor'''
  _a  : int      			=     ('''CLIPTokenizer''', '''CLIPTokenizerFast''')







  def __init__(			self   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	**UpperCamelCase__	):






         """simple docstring"""


         a_						     =			None
         if "feature_extractor" in kwargs:
                warnings.warn(
                    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                    ' instead.'   ,	UpperCamelCase__   ,	)
                a_						     =			kwargs.pop('feature_extractor'	)

         a_						     =			image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
                raise ValueError('You need to specify an `image_processor`.'	)
         if tokenizer is None:
                raise ValueError('You need to specify a `tokenizer`.'	)

         super().__init__(UpperCamelCase__   ,	UpperCamelCase__	)







  def __call__(			self   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	**UpperCamelCase__	):






         """simple docstring"""


         if text is None and visual_prompt is None and images is None:
                raise ValueError('You have to specify either text, visual prompt or images.'	)

         if text is not None and visual_prompt is not None:
                raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'	)

         if text is not None:
                a_						     =			self.tokenizer(UpperCamelCase__   ,	return_tensors=UpperCamelCase__   ,	**UpperCamelCase__	)

         if visual_prompt is not None:
                a_						     =			self.image_processor(UpperCamelCase__   ,	return_tensors=UpperCamelCase__   ,	**UpperCamelCase__	)

         if images is not None:
                a_						     =			self.image_processor(UpperCamelCase__   ,	return_tensors=UpperCamelCase__   ,	**UpperCamelCase__	)

         if visual_prompt is not None and images is not None:
                a_						     =			{
                    'pixel_values': image_features.pixel_values,
                    'conditional_pixel_values': prompt_features.pixel_values,
                }
                return encoding
         elif text is not None and images is not None:
                a_						     =			image_features.pixel_values
                return encoding
         elif text is not None:
                return encoding
         elif visual_prompt is not None:
                a_						     =			{
                    'conditional_pixel_values': prompt_features.pixel_values,
                }
                return encoding
         else:
                return BatchEncoding(data=dict(**UpperCamelCase__	)   ,	tensor_type=UpperCamelCase__	)







  def    _a       (			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         return self.tokenizer.batch_decode(*UpperCamelCase__   ,	**UpperCamelCase__	)







  def    _a       (			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         return self.tokenizer.decode(*UpperCamelCase__   ,	**UpperCamelCase__	)







  @property
  def    _a       (			self	):






         """simple docstring"""


         warnings.warn(
             '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'   ,	UpperCamelCase__   ,	)
         return self.image_processor_class







  @property
  def    _a       (			self	):






         """simple docstring"""


         warnings.warn(
             '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'   ,	UpperCamelCase__   ,	)
         return self.image_processor




